Bias mitigation ensures that AI systems render decisions characterized by impartiality and fairness, irrespective of variables such as race, gender, or other sensitive attributes.
· Thibault Dody, Patrick Doherty
Regardless of what part of your business you find yourself in, your team has likely discussed implementing artificial intelligence (AI) of some sort in 2024–and it’s no wonder, with 33% of businesses already using AI and another 42% considering adopting it, the benefits of harnessing AI are too good to be ignored.
· Patrick Doherty
Bias can rear its ugly head in various forms, from implicit biases that occur without the human actor being aware of it, to conscious bias in the inverse. While bias in data is historically more prevalent in certain B2C industries, like financial services, it doesn’t exclusively live there.
· Michael Musty, Patrick Doherty
The central idea of Responsible AI is to understand our models—how they work, why they produce the results they do—enough to identify and correct their shortcomings.
· Patrick Doherty
Join Directors of Accounts Alex Noyes and Nick Haggerty, as well as Director of Marketing, Perry McDermott and Senior Data Scientist, Thibault Dody as they discuss real applications for probability scores.
· Faraday
Prediction systems finding silly patterns is a real problem—you could even say it's _the_ problem of machine learning. Luckily there are great ways to avoid it.
· Andy RossmeisslThis site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.